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| Estimator Common Correlated Effects Mean Group (CCEMG)× | Estimator Grup Rata-rata yang Diperluas (AMG)× | Regresi Kuadrat Terkecil Biasa (Ordinary Least Squares - OLS)× | |
|---|---|---|---|
| Bidang | Ekonometrika | Ekonometrika | Ekonometrika |
| Keluarga | Regression model | Regression model | Regression model |
| Tahun asal≠ | 2006 | 2010 | 2019 |
| Pencetus≠ | M. Hashem Pesaran | Eberhardt & Teal; Bond & Eberhardt | Wooldridge (textbook treatment); classical least squares |
| Tipe≠ | Heterogeneous panel estimator | Heterogeneous panel data estimator | Linear regression |
| Sumber perintis≠ | Pesaran, M. H. (2006). Estimation and Inference in Large Heterogeneous Panels with a Multifactor Error Structure. Econometrica, 74(4), 967-1012. DOI ↗ | Eberhardt, M. & Teal, F. (2010). Productivity Analysis in Global Manufacturing Production. Economics Series Working Papers, No. 515, University of Oxford. link ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Alias≠ | common correlated effects, CCE, CCEMG, Pesaran CCE estimator | AMG estimator, augmented mean group, Artırılmış Ortalama Grup Tahmincisi (AMG) | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Terkait≠ | 4 | 4 | 5 |
| Ringkasan≠ | The Common Correlated Effects Mean Group estimator, introduced by Pesaran in 2006, is a heterogeneous panel-data estimator that controls for cross-sectional dependence by approximating unobserved common factors with the cross-section averages of the variables. It remains consistent when the slope coefficients differ across units. | The Augmented Mean Group estimator, developed by Eberhardt and Teal (2010), is a panel data method for estimating heterogeneous slope coefficients in the presence of cross-sectional dependence. It approximates the unobserved common dynamic process driving all units and folds it into unit-by-unit regressions, then averages the results. | Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE). |
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